For records managers and others responsible for building and enforcing classification policies, retention schedules, and other aspects of records management plan, the problem with traditional, manual classification methods can be overwhelming.
Content needs to be classified or understood in order to determine why it must be retained, how long it must be retained, and when it can be dispositioned. Managing the retention and disposition of information reduces litigation risk, reduces discovery and storage costs, and ensures that organizations maintain regulatory compliance.
Classification is the last thing end-users want (or are able) to do. Users see the process of sorting records from transient content as intrusive, complex, and counterproductive. On top of this, the popularity of mobile devices and social media applications has effectively fragmented the content authoring market and has eliminated any chance of building consistent classification tools into end-user applications.
However, if classification is not being carried out there are serious implications when asked by regulators or auditors to provide reports to defend the organization’s records and retention management program.
User concerns aside, records managers also struggle with enforcing policies that rely on manual, human-based approaches. Accuracy and consistency in applying classification is often inadequate when left up to users, the costs in terms of productivity loss are high, and these issues, in turn, result in increased business and legal risk as well as the potential for the entire records management program to quickly become unsustainable in terms of its ability to scale.
So what is the answer? How can organizations overcome the challenges posed by classification?
The answer is a solution that provides automatic identification, classification, retrieval, archival, and disposal capabilities for electronic records as required by the records management policy.
OpenText Auto-Classification is the solution that combines records management with cutting edge semantic capabilities for classification of content. It eliminates the need for users to manually identify records and apply requisite classification. By taking the burden of classification off users, records managers can improve consistency of classification and better enforce rules and policies.
OpenText Auto-Classification makes it possible for records managers to easily demonstrate a defensible approach to classification based on statistically relevant sampling and quality control. Consequently, this minimizes the risk of regulatory fines and eDiscovery sanctions.
It provides a solution that eliminates the need for users to sort and classify a growing volume of content while offering records managers and the organization as a whole the ability to establish completely transparent records management program as part of their broader information governance strategy.
Auto-Classification uses OpenText analytics engine to go through documents and codifies language-specific nuances identified by linguistic experts.
Automated Classification: Automate the classification of content in OpenText Content Server inline with existing records management classifications.
Advanced Techniques: Classification process based on a hybrid approach that combines machine learning, rules, and content analytics.
Flexible Classification: Ability to define classification rules using keywords or metadata.
Policy-Driven Configuration: Ability to configure and optimize the classification process with an easy step-by-step tuning guide.
Advanced Optimization Tools: Reports make it easy to examine classification results, identify potential accuracy issues, and then fix those issues by leveraging the provided optimization hints.
Sophisticated Relevancy and Accuracy Assurance: Automatic sampling and benchmarking with a complete set of metrics to assess the quality of the classification process.
Quality Assurance Workbench: Advanced reports on a statistically relevant sample to review and code documents that have been automatically classified to manually assess the quality of the classification results when desired.
Auto-Classification works with OpenText Records Management so existing classifications and documents can be used during the tuning process.
OpenText Auto-Classification was developed in close partnership with customers using the OpenText ECM Suite, and works in conjunction with OpenText Records Management so that existing classifications and classified documents can be used in the tuning process.